Data Lakes are too hard for most enterprises. Data Lakes are a foundational capability to get fast access to data and enable AI/ML, data engineering, and all analytics, but they require specialized expertise that is hard to find and retain. Data Lakes also take a long time, typically 6+ months of upfront development followed by ongoing management, support and optimization as the stack keeps evolving. That's why, for the past five years, the Cazena team has been developing the first SaaS Data Lake as a Service.
451 Research Vice President Matt Aslett offers a timely update on cloud data lakes in this guest blog. Despite criticism, the data lake has emerged in the last decade as a solution to managing large volumes of data, and is relatively widely adopted. While simple on paper, the data lake is a lot more difficult to deliver in practice, with multiple moving parts that can fragment without significant manual intervention. Data processing is shifting to the cloud, but IaaS and PaaS data lake environments still require a lot of manual intervention on the part of the user. True SaaS data lake environments mask the complexity, enabling users to concentrate on data processing and analysis rather than infrastructure management and software orchestration.
Cazena founder Prat Moghe was featured in a video by Amazon Web Services (AWS) on the AWS Partner Network (APN) blog and spotlight page. The video showcases the success that selected AWS startups have had with enterprises. As the first SaaS Data Lake as a Service, Cazena has made a fast and measurable impact for enterprises moving to AWS.
Cazena has been named among the first AWS Technology Partners for the launch of the ISV Workload Migration Program. Many enterprises rely on Cazena to speed and simplify migrations to AWS. As the first SaaS Data Lake as a Service, migrations with Cazena don’t require additional staff or specialized skills. Learn more about the benefits of migrating to AWS with Cazena.
Cazena is proud to announce our latest release of our core Big Data as a Service. This release of Cazena is the culmination of the past four incremental releases, all focused on security. This version delivers a new security architecture that enables end-to-end TLS encryption as well as many other security capabilities, available in all of Cazena’s managed SaaS solutions.
Barcelona, well known for its architectural masterpieces, is hosting a different type of architect this week – data architects. At this week’s DataWorks Summit in Barcelona, Cazena will be mixing with distinguished data architects along with experts in cloud, big data, Hadoop, analytics, data science, machine learning, and more.
Cazena and Cloudaeon are pleased to announce a partnership that extends the power of Cazena’s cloud-based Big Data as a Service and Cloudaeon’s automated analytics and demand forecasting solutions. The partnership is aimed at helping companies in retail, supply chain and logistics more effectively use data and analytics for strategic activities.
When you deliver product incrementally, in an agile fashion, it is often hard to see, week to week, sprint to sprint, all the great progress made throughout the year. That’s why, at the end of every year, I enjoy looking back at what my teams and company have accomplished the past twelve months. For Cazena it was an exceptional year with triple-digit growth of our customer subscriber base. Behind the curtain, the data paints an interesting picture of what it means to provide this growing customer base with a fully-managed, secure, highly-available big data platform.
We are pleased to announce a new partnership between Cazena and Eden Smith, the leading UK big data consulting and staffing specialist. The companies share a strategic mission of empowering data leaders and accelerating analytic impact. Eden Smith is known for deep market knowledge, strategy expertise and a vetted roster of data and analytics talent, with a specific focus on Chief Data Officers. Cazena’s Big Data as a Service solutions are fully-managed, secured and automated, offering enterprise data lakes, analytics and data science platforms at ½ the cost of alternatives.